Function test_logical_and

tests/python/relax/test_frontend_from_exported_program.py:1019–1044

()

def test_logical_and():
    class LogicalAnd(Module):
        def forward(self, lhs, rhs):
            return torch.logical_and(lhs, rhs)

    @tvm.script.ir_module
    class expected:
        @R.function
        def main(
            lhs: R.Tensor((1, 3, 10, 10), dtype="float32"),
            rhs: R.Tensor((1, 3, 10, 10), dtype="float32"),
        ) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="bool")):
            # block 0
            with R.dataflow():
                lv: R.Tensor((1, 3, 10, 10), dtype="bool") = R.astype(lhs, dtype="bool")
                lv1: R.Tensor((1, 3, 10, 10), dtype="bool") = R.astype(rhs, dtype="bool")
                lv2: R.Tensor((1, 3, 10, 10), dtype="bool") = R.logical_and(lv, lv1)
                gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="bool")) = (lv2,)
                R.output(gv)
            return gv

    example_args = (
        torch.randn(1, 3, 10, 10, dtype=torch.float32),
        torch.randn(1, 3, 10, 10, dtype=torch.float32),
    )
    verify_model(LogicalAnd(), example_args, {}, expected)


def test_logical_not():